We conducted a single-center retrospective observational study of 647 postoperative NSCLC patients to investigate the association between the expression of PD-L1 and postoperative recurrence. In our cohort, the recurrence rate was 23.3%, consistent with the previously reported rates of 20–26% in larger cohorts studying lung cancer23,24. Our study showed that groups with higher PD-L1 expression levels had shorter RFS in the conventional classification based on PD-L1 expression levels (no expression [< 1%], low expression [1–49%], and high expression [50–100%]). In addition, using a machine learning model with a random forest algorithm and a multivariate Cox proportional hazards model with a statistical analysis, we investigated the impact of the PD-L1 expression on the postoperative recurrence of NSCLC. Our results showed a nonlinear increase in the risk of postoperative recurrence based on the PD-L1 expression level.
Including our previous study and several conventional studies, there have been reports linking the PD-L1 expression in NSCLC to an increased risk of postoperative recurrence10,25–28. However, the results of our study may help to further develop this association. In previous studies, a statistical approach demonstrated an association between the expression of PD-L1 in NSCLC and postoperative recurrence. This association was similarly observed in the multivariate Cox proportional hazards analysis in our study, which is consistent with previous research. In contrast to previous studies, our study statistically demonstrated an increase in postoperative recurrence risk corresponding to each 1% increase in the PD-L1 expression level by conducting statistical analysis treating the PD-L1 expression as a continuous variable rather than as a categorical variable. Furthermore, we used a machine learning approach to evaluate the detailed effect of the PD-L1 expression on postoperative recurrence. Specifically, we revealed in detail the dynamic changes in postoperative recurrence depending on the PD-L1 expression level using a SHAP analysis and partial dependence plots. Our machine-learning approach has discovered a new finding that has not been reported in previous statistical analyses. This finding highlights the potential for dynamic variations in postoperative recurrence of NSCLC associated with PD-L1 expression levels. In conventional studies, the relationship between the expression of PD-L1 and the postoperative prognosis has been examined using categorical classifications such as no expression (< 1%), low expression (1–49%), and high expression (≥ 50%). However, our study, leveraging machine learning techniques, revealed a more nuanced, continuous association between PD-L1 expression levels and recurrence risk that could not be captured by these traditional categories. The nonlinear increase in the risk of recurrence with even minimal PD-L1 levels (as low as 1%) and the linear escalation of risk when the expression increased beyond 1% suggest the importance of considering the PD-L1 expression as a continuous variable rather than discrete category. This finding underscores the potential for dynamic variations in the postoperative recurrence risk across the spectrum of PD-L1 expression, providing a more granular understanding of the relationship between the expression of PD-L1 and the recurrence of NSCLC. The results of this study may contribute to its clinical application. It has been reported that adjuvant chemotherapy with immune checkpoint inhibitors during the perioperative period of lung cancer improves a patient’s prognosis, even with PD-L1 expression levels as low as 1%, and this effect becomes more pronounced when the expression levels exceeds 50%29. The nonlinear and dynamic changes in the risk of recurrence based on PD-L1 expression levels may encourage the active introduction of perioperative immune checkpoint inhibitors for PD-L1-positive lung cancer patients as a clinical decision-making strategy.
The results of this study can be explained from an immunological perspective. We showed that the contribution to recurrence increased non-linearly and sharply when PD-L1 was expressed, even at 1%, in comparison to PD-L1-negative cases. This trend suggests that, even the minimal expression of PD-L1 may have a significant impact on postoperative recurrence in NSCLC. Even when cancer cells express PD-L1 at 1%, subtle interactions between PD-L1-expressing cancer cells and the surrounding immune cells may lead to local immune suppression. This local suppression of immune cell activity may increase the immune resistance of cancer cells, activate local immune escape mechanisms and potentially increase the risk of postoperative recurrence. This possibility is supported by clinical studies showing the higher efficacy of immune checkpoint inhibitors in cases where PD-L1 is expressed, even at 1%, in comparison to cases with no expression30,31. In contrast, in cases with PD-L1 expression levels of ≥ 1%, a linear increase in the contribution to postoperative recurrence was observed with increasing expression levels. This mechanism suggests that, as the number of cancer cells expressing PD-L1 increases, the suppression of immune cell activity increases, resulting in more immune-resistant cancer cells and further activation of immune escape mechanisms. This possibility is supported by clinical trials that demonstrate the higher efficacy of immune checkpoint inhibitors in patients with high PD-L1 expression levels (≥ 50%) in comparison to those with low expression levels (1–49%) and PD-L1-negative cases (< 1%)31–33. In addition to direct interactions with immune cells, the involvement of PD-L1 in the formation of the tumor microenvironment could be a potential mechanism by which different PD-L1 expression levels exert a nonlinear influence on the risk of recurrence. PD-L1 is capable of nuclear translocation and has been reported to directly bind to DNA and regulate the transcriptional induction of genes involved in the tumor microenvironment, such as immune responses and inflammation. In other words, when PD-L1 is expressed, the higher its expression level, the more likely it is that signaling pathways associated with tumor immune evasion are activated, potentially contributing to the establishment of an immunosuppressive tumor microenvironment. The formation of the tumor microenvironment may be closely related to the survival and proliferation of residual cancer cells, possibly increasing the risk of postoperative recurrence34. The dynamic changes in the increased risk of recurrence as a function of PD-L1 expression levels revealed in our study suggest significant variations in the risk of recurrence, even within the PD-L1 expression categories of low (1–49%) and high (50–100%) that are commonly used in clinical practice. Our study is the first to reveal the machine-learning-based dynamic variation of postoperative recurrence risk dependent on the continuous range of PD-L1 expression levels from low to high expression.
This study was associated with several limitations. First, as this was a retrospective observational study conducted at a single institution, caution should be exercised in generalizing the results. Given its retrospective nature, it is a possible that clinical factors other than the expression of PD-L1 were not considered. Future validation through additional studies, such as prospective investigations or multicenter collaborations, is essential to elucidate the impact of these factors. Second, the performance of machine learning models is strictly limited by predictive accuracy and requires careful consideration in clinical applications. The results of this study, derived from a limited dataset, require further validation in clinical research using other cohorts or larger datasets to determine whether similar trends can be observed in recurrence prediction models. Overall, these considerations highlight the need for cautious interpretation and for future research to improve the robustness and applicability of our findings.